8 GeV Planning 16 June 2020 Ruth Pttgen Overview - use Peters - - PowerPoint PPT Presentation

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8 GeV Planning 16 June 2020 Ruth Pttgen Overview - use Peters - - PowerPoint PPT Presentation

Jun 14, 2023 211 likes •329 views

8 GeV Planning 16 June 2020 Ruth Pttgen Overview - use Peters selection at 4 GeV - Trigger threshold - Trigger skimming (re-reco?) - BDT training (mixed signal + PN) - re-reco to include BDT result - determine BDT cut - apply vetoes, see

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SLIDE 1

8 GeV Planning

16 June 2020 Ruth Pöttgen

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SLIDE 2

16 June 2020

LDMX

Overview

  • 2
  • use Peter’s selection at 4 GeV
  • Trigger threshold
  • Trigger skimming (re-reco?)
  • BDT training (mixed signal + PN)
  • re-reco to include BDT result
  • determine BDT cut
  • apply vetoes, see what is left
  • if necessary, apply MIP tracking to remaining events

+ apply to target PN samples

+ re-do Tom’s studie on shower composition

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SLIDE 3

16 June 2020

LDMX

Peter’s selection

  • 3

to do:

  • run Peter’s selection on both 8 and 4 GeV
  • produce a cutflow

Goal: get a more or less direct first comparison of 8 GeV vs 4 GeV

Time estimate: 1-2 weeks

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SLIDE 4

16 June 2020

LDMX

Trigger Threshold

  • 4

Trigger Missing Energy [MeV]

500 1000 1500 2000 2500 3000 3500 4000

Efficiency

5 −

10

4 −

10

3 −

10

2 −

10

1 −

10 1 Sum of Layers 1-20 = 1 MeV

A'

m = 5 MeV

A'

m = 10 MeV

A'

m = 1000 MeV

A'

m

  • Inclusive 4 GeV e

@4 GeV

not sure why this is not 1 (figure caption says it is the “trigger rate for all backgrounds induced by beam electrons”)

trigger missing energy: beam energy - energy in first 20 layers

to do:

  • run reco for inclusive samples (~1e7 events in Lund)

/projects/hep/fs9/lunarc_nobackup_users/ pflorido/ldmx20Apr/mc-data/v9/8.0GeV/ mc_v9-8GeV-1e-inclusive*

  • write an analyser script for inclusive background

based on e.g. existing PN analyser

  • implement calculation of energy sum in first 20 layers
  • produce plot like the one on the right for signal and

inclusive background sample —> determine reasonable trigger threshold Goal: determine cut on energy in first 20 ECal layers

Time estimate: ~2 weeks

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SLIDE 5

16 June 2020

LDMX

Trigger Skimming

  • 5

to do:

  • re-reco (Lene can provide an example?) with trigger skimming in place
  • in reco config things like

from LDMX.EventProc.simpleTrigger import simpleTrigger simpleTrigger.parameters["threshold"] = <triggerThreshold/MeV> simpleTrigger.parameters["end_layer"] = 20 p.sequence=[ecalDigis, …, simpleTrigger, …] p.skimDefaultIsDrop() p.skimConsider("simpleTrigger")

Goal: reduce sample size, produce inputs for BDT training Time estimate: ~1 week

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SLIDE 6

16 June 2020

LDMX

BDT Training + Re-reco

  • 6

to do:

  • produce training samples
  • mix events from 4 signal samples with different masses
  • subset of ECal PN backgrounds
  • follow Valentina’s instructions (see backup)

—> produces a new pkl file

  • re-reco with that pkl file to get BDT result in output

Goal: retrain Gabrielle BDT on 8 GeV (i.e. no new variables etc.) and determine cut value Time estimate: ~3 weeks

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SLIDE 7

16 June 2020

LDMX

Veto Sequence

  • 7

to do:

  • determine BDT threshold to be used (signal efficiency vs. background rejection)
  • adapt PN analyser to include vetoes as in paper
  • trigger
  • single track
  • BDT veto
  • HCal veto (leave unchanged for first try)
  • see what remains
  • potentially modify HCal veto
  • check MIP tracking

Goal: repeat ~same event selection as for 4 GeV veto paper Time estimate: ~2-3 weeks

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SLIDE 8

16 June 2020

LDMX

Target PN

  • 8

to do:

  • run the same selection on target PN samples

Goal: Get estimate of target PN background Time estimate: ~1 week

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SLIDE 9

16 June 2020

LDMX

Shower Composition

  • 9

to do:

  • reproduce Fig 7 from the paper
  • generate high-stats (nearly) inclusive sample
  • disable certain processes
  • divide events into categories as in Fig 7

Goal: Check conclusion obtained at 4 GeV, that low-energy tail of showers is dominated by events with high-energy photon going PN Time estimate: ~2 weeks (depends on how fast we can generate)

500 1000 1500 2000 2500 3000 3500 4000 Reconstructed Energy in ECAL [MeV]

14 −

10

12 −

10

10 −

10

8 −

10

6 −

10

4 −

10

2 −

10 1 Event Rate

LDMX Simulation

= Total Energy Going PN

PN

E All Events < 50MeV

PN

E < 1.2GeV

PN

50MeV < E < 2.8GeV

PN

1.2GeV < E 2.8GeV ≥

PN

E

ECal Trigger

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SLIDE 10

Additional Material

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SLIDE 11

16 June 2020

LDMX

Valentina’s Instructions

  • 11

The scripts used for the paper version can be found here: https://github.com/IncandelaLab/LDMX-scripts/tree/master/EcalVeto bdtTreeMaker.py makes flat ROOT trees to be used for training/ECAL studies (the paths to the ldmx-sw install, input files and output directory can be passed as arguments) bdtMaker.py is used to actually train the BDT from the training subsets that you want to use bdtEval.py can be used to evaluate the BDT score per-event and add it to the flat trees I added a README at some point for our students which should provide step- by-step instructions for the training process: https://github.com/IncandelaLab/LDMX-scripts/blob/master/README.md